Multi-scale governance and data for sustainable development

Area of Science:

• Computational Social Science
• Digital Governance
• Data Science

Background:

• Future societies face complexity from the 5th Industrial Revolution and 2nd Data Revolution.
• Emerging digital systems and the Metaverse necessitate new approaches to human behavior and collective action.
• Existing governance models are insufficient for managing data-driven societal complexities.

Purpose of the Study:

• To explore the role of digital policy and governance in navigating societal complexity.
• To propose multi-scale governance as a paradigm for the Data Revolution.
• To outline principles for designing resilient digital societies through data innovation and AI.

Main Methods:

• Analysis of multi-scale systems (biological, migratory) through data.
• Conceptualization of multi-scale governance integrating top-down and bottom-up approaches.
• Framework for AI-driven digital platforms for data curation and policy implementation.

Main Results:

• Digital technologies enable new computational methods to address inequality while preserving rights.
• Multi-scale governance is essential for a highly digitalized world.
• Coordinating top-down agencies with bottom-up digital platforms is a key design principle.

Conclusions:

• Robust digital societies depend on effective development of complexity and governance.
• AI-driven platforms are crucial for implementing data-driven policies for sustainable development.
• Collective Intelligence, powered by data innovation, is fundamental for future societal resilience.